FrontierThe story, in brief
DeepSeek AI Released DeepSeek-V4.1-Flash with 1M Context, FP4 KV Cache, and Cross-Layer Attention Reuse
DeepSeek-V4.1-Flash: 1M context, FP4 KV cache compression, and cross-layer attention reuse — built for the agent era's memory wall.
The KeyNews take
Why it matters
DeepSeek engineered a new model class around the real bottleneck long-horizon agents expose: KV cache memory and bandwidth strain. FP4 quantization and attention reuse are hard technical moves that practitioners deploying agent systems will evaluate immediately.
The key facts
7 to knowDeepSeek-V4.1-Flash: 552B backbone + 196B Engram parameters
1M-token context window
FP4 KV cache quantization (addresses HBM/bandwidth bottleneck)
Cross-layer attention reuse mechanism
Multimodal Mixture-of-Experts architecture
Optimized for long-horizon agent workloads with repeated prefills
Published September 10, 2026
Go to the source
MarkTechPostmarktechpost.com
Publisher excerpt: Long-horizon agents have turned LLM serving into an input-heavy workload. Repeated prefills and million-token contexts leave KV caches that strain HBM, SSD capacity, and bandwidth. DeepSeek AI built its newest release around that exact bottleneck. DeepSeek-V4.1-Flash is a multimodal…